Mohammadi and Saniee Abadeh in [1]} proposed a new hybrid approach to steganalysis named, region based Image Steganalysis using Artificial Bee colony (RISAB). RISAB enables ABC to search in image space, particularly spatial domain, to find the most probable sub-image to carry the hidden messages. As a matter of fact, the likelihood of embedding messages in a sub-image is high if the amount of intensity and energy is greater than the other parts of the image. RISAB is a combination of applying IFAB over the whole image, and the sub-image selected by ABC. The first main step and goal of their paper is to investigate the whole image to identify such a high energy sub-image leveraging a customized ABC to go through the spatial domain of the image. Then, they extract features twice. First, they extract features from the whole given image. Second, they extract the same features from the sub-image found in the earlier phase. They apply the same features that IFAB [2]} presents, which are the best subset of features for feature extractors, SPAM and CC-PEV. Having extracted the features from the whole image and sub-image, researchers merge the datasets for both feature extractors separately. The final dataset for SPAM and CC-PEV involves 160 and 500 features, respectively. In either datasets, features visualize the instances in a way that the classifier will be able to train and find a high-performance model. Since authors considered both the whole images and the associated sub-images, their approach greatly outperforms IFAB.
